Timeline / Data.gov — Health Datasets
changed Source changed
A new raw object was archived. Both versions are preserved. 5137 line(s) added, 4339 line(s) removed.
Evidence
| Source | Data.gov — Health Datasets |
|---|---|
| Agency | Data.gov |
| URL | https://api.gsa.gov/technology/datagov/v4/search?q=health&sort=last_harvested_date&per_page=100&api_key=${DATAGOV_API_KEY} |
| Observed by | Civic Memory, directly, on 2026-09-24T00:26:03+00:00 |
| Content type | application/json |
| Current object |
ce57c42bc1ee3881e51677fd142c3b885d936de564eef5441cc9f71b9de4c242
download raw
metadata
|
| Previous object |
e50fcf16fd59610fe8c1d05931daf1cc77ee613924da765199bc33f62d457120
download raw
metadata
|
What changed derived
This diff is not evidence. It was produced by
civic-memory.diff_engine 1.1.0 at
2026-09-24T00:26:03+00:00 by normalizing the two archived objects above. The
objects are authoritative; this reading of them can be regenerated or deleted
without loss. 5137 line(s) added, 4339 line(s) removed.
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They also add additional years of data and options for selecting different age ranges and benchmarks.\r\n\r\nPotentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths.\r\n\r\nNot all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services.\r\n\r\nMortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map.\r\n\r\nUnderlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10)\r\n\r\nHeart disease (I00-I09, I11, I13, and I20–I51)\r\nCancer (C00–C97)\r\nUnintentional injury (V01–X59 and Y85–Y86)\r\nChronic lower respiratory disease (J40–J47)\r\nStroke (I60–I69)\r\nLocality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme.\r\n\r\nBenchmarks are based on the three states with the lowest age and cause-specific mortality rates.\r\n\r\nPotentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths.\r\n\r\nUsers can explore three benchmarks:\r\n\r\n“2010 Fixed” is a fixed benchmark based on the best performing States in 2010.\r\n“2005 Fixed” is a fixed benchmark based on the best performing States in 2005.\r\n“Floating” is based on the best performing States in each year so change from year to year.\r\n \r\nSOURCES\r\n\r\nCDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).\r\n\r\nREFERENCES \r\n\r\n1. Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8.\r\n\r\n2. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.", "distribution": [ { "@type": "dcat:Distribution", - "accessURL": "https://gems.lm.doe.gov/arcgis/rest/services/Site_Features/MapServer", - "description": "REST API for querying LM data", - "format": "API", - "title": "REST API" - }, - { - "@type": "dcat:Distribution", - "accessURL": "https://gems.lm.doe.gov/arcgis/rest/services/Site_Features/MapServer/10/?f=pjson", - "description": "Each feature within this dataset is the authoritative representation of the location of a sample within the U.S. Department of Energy (DOE) Office of Legacy Management (LM) Environmental Database. 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These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks.\r\n\r\nPotentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths.\r\n\r\nNot all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services.\r\n\r\nMortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map.\r\n\r\nUnderlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10)\r\n\r\nHeart disease (I00-I09, I11, I13, and I20–I51)\r\nCancer (C00–C97)\r\nUnintentional injury (V01–X59 and Y85–Y86)\r\nChronic lower respiratory disease (J40–J47)\r\nStroke (I60–I69)\r\nLocality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme.\r\n\r\nBenchmarks are based on the three states with the lowest age and cause-specific mortality rates.\r\n\r\nPotentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths.\r\n\r\nUsers can explore three benchmarks:\r\n\r\n“2010 Fixed” is a fixed benchmark based on the best performing States in 2010.\r\n“2005 Fixed” is a fixed benchmark based on the best performing States in 2005.\r\n“Floating” is based on the best performing States in each year so change from year to year.\r\n \r\nSOURCES\r\n\r\nCDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).\r\n\r\nREFERENCES \r\n\r\n1. 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Estimates less than 10 are suppressed. These early model-based provisional estimates were generated using a multi-stage hierarchical Bayesian modeling process to generate smoothed estimates of the weekly numbers of death, accounting for reporting lags. These estimates are based on several assumptions about how the reporting lags have changed in recent months across different jurisdictions, and the resulting estimates differ from other sources of provisional mortality data. For now, these estimates should be considered highly uncertain until further evaluations can be done to determine the validity of these assumptions about timeliness. The true patterns in reporting lags will not be known until data are finalized, typically 11–12 months after the end of the calendar year. Importantly, these estimates are not a replacement for monthly provisional drug overdose death counts, or quarterly provisional mortality estimates. For more detail about the nowcasting methods and models, see:\n\nRossen LM, Hedegaard H, Warner M, Ahmad FB, Sutton PD. Early provisional estimates of drug overdose, suicide, and transportation-related deaths: Nowcasting methods to account for reporting lags. Vital Statistics Rapid Release; no 11. Hyattsville, MD: National Center for Health Statistics. February 2021. 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For more detail about the nowcasting methods and models, see:\n\nRossen LM, Hedegaard H, Warner M, Ahmad FB, Sutton PD. Early provisional estimates of drug overdose, suicide, and transportation-related deaths: Nowcasting methods to account for reporting lags. Vital Statistics Rapid Release; no 11. Hyattsville, MD: National Center for Health Statistics. February 2021. 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Along with developing the fundamentals of being able to confidently predict Remaining Useful Life (RUL), the technology calls for fielded applications as it inches towards maturation. This requires a stringent performance evaluation so that the significance of the concept can be fully exploited. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few issues. Instead, the research community has used a variety of metrics based largely on convenience with respect to their respective requirements. Very little attention has been focused on establishing a common ground to compare different efforts. This paper surveys the metrics that are already used for prognostics in a variety of domains including medicine, nuclear, automotive, aerospace, and electronics. It also considers other domains that involve prediction-related tasks, such as weather and finance. Differences and similarities between these domains and health maintenance have been analyzed to help understand what performance evaluation methods may or may not be borrowed. Further, these metrics have been categorized in several ways that may be useful in deciding upon a suitable subset for a\r\nspecific application. Some important prognostic concepts have been defined using a notational framework that enables interpretation of different metrics coherently. Last, but not the \r\nleast, a list of metrics has been suggested to assess critical aspects of RUL predictions before they are fielded in real applications.", + "fn": "National Center for Health Statistics", + "hasEmail": "mailto:cdcinfo@cdc.gov" + }, + "description": "This file contains COVID-19 death counts and rates by jurisdiction of residence (U.S., HHS Region) and demographic characteristics (sex, age, race and Hispanic origin, and age/race and Hispanic origin). United States death counts and rates include the 50 states, plus the District of Columbia. \n\nDeaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file.\n\nData during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death.\n\nDeath counts should not be compared across jurisdictions. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. \n\nThe ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington.\n\nRates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf).\n\nRate are based on deaths occurring in the specified week and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly age-adjusted rates which have been adjusted to allow comparison with annual rates. 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Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).", "distribution": [ { "@type": "dcat:Distribution", - "description": "PHM_2008_Metrics.pdf", - "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/PHM_2008_Metrics.pdf", - "format": "application/force-download", - "mediaType": "application/force-download", - "title": "PHM_2008_Metrics.pdf" + "downloadURL": "https://data.cdc.gov/api/v3/views/dmnu-8erf/export.csv?accessType=DOWNLOAD", + "mediaType": "text/csv" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://data.cdc.gov/api/v3/views/dmnu-8erf/query.json?accessType=DOWNLOAD", + "mediaType": "application/json" + }, + { + "@type": "dcat:Distribution", + "downloadURL": "https://data.cdc.gov/api/v3/views/dmnu-8erf/query.xml?accessType=DOWNLOAD", + "mediaType": "application/xml" } ], - "identifier": "DASHLINK_393", - "issued": "2011-06-07", - "keyword": [ - "ames", - "dashlink", - "nasa" - ], - "landingPage": "https://c3.nasa.gov/dashlink/resources/393/", - "modified": "2025-03-31", + "identifier": "https://data.cdc.gov/api/views/dmnu-8erf", + "issued": "2023-05-10", + "keyword": [ + "coronavirus", + "covid-19", + "death rate", + "deaths", + "hhs region", + "mortality", + "nchs", + "nvss", + "united states", + "weekly" + ], + "landingPage": "https://data.cdc.gov/d/dmnu-8erf", + "license": "https://www.usa.gov/government-works", + "modified": "2026-09-22", "programCode": [ - "026:029" + "009:020" ], "publisher": { "@type": "org:Organization", - "name": "Dashlink" - }, - "title": "Metrics for Evaluating Performance of Prognostics Techniques" - }, - "description": "Prognostics is an emerging concept in condition based maintenance (CBM) of critical systems. 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United States death counts and rates include the 50 states, plus the District of Columbia. \n\nDeaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file.\n\nData during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death.\n\nDeath counts should not be compared across jurisdictions. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. \n\nThe ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington.\n\nRates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf).\n\nRate are based on deaths occurring in the specified week and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly age-adjusted rates which have been adjusted to allow comparison with annual rates. 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Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).", + "distribution_titles": [], + "harvest_record": "https://catalog.data.gov/harvest_record/a560803e-b41e-467e-8eff-0867757aadf5", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/a560803e-b41e-467e-8eff-0867757aadf5/raw", "has_download": true, - "has_spatial": false, - "identifier": "DASHLINK_393", - "keyword": [ - "ames", - "dashlink", - "nasa" - ], - "last_harvested_date": "2026-09-23T01:31:53.402911", + "has_spatial": true, + "identifier": "https://data.cdc.gov/api/views/dmnu-8erf", + "keyword": [ + "coronavirus", + "covid-19", + "death rate", + "deaths", + "hhs region", + "mortality", + "nchs", + "nvss", + "united states", + "weekly" + ], + "last_harvested_date": "2026-09-23T21:24:14.110297", "organization": { "aliases": [ - "" + "US", + "dept" ], "code_repo_exempt": false, "code_repo_url": null, "description": null, - "id": "f4ca4614-8901-409b-8553-2e994ad10023", - "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/nasa.png", - "name": "National Aeronautics and Space Administration", + "id": "2c2fc21f-21d0-4450-af01-cf8c69b44156", + "logo": "https://raw.githubusercontent.com/GSA/logo/refs/heads/master/hhs.png", + "name": "U.S. Department of Health & Human Services", "organization_type": "Federal Government", - "slug": "nasa" + "slug": "hhs" }, "parent_identifier": null, - "popularity": 2, - "publisher": "Dashlink", - "slug": "metrics-for-evaluating-performance-of-prognostics-techniques", - "spatial_centroid": null, - "spatial_shape": null, - "theme": [], - "title": "Metrics for Evaluating Performance of Prognostics Techniques", + "popularity": 15, + "publisher": "Centers for Disease Control and Prevention", + "slug": "provisional-covid-19-death-counts-and-rates-by-jurisdiction-of-residence-and-demographic-c", + "spatial_centroid": { + "lat": 34.4819914, + "lon": -101.6218762 + }, + "spatial_shape": { + "coordinates": [ + [ + [ + [ + -124.733253, + 24.544245 + ], + [ + -124.733253, + 49.388611 + ], + [ + -66.954811, + 49.388611 + ], + [ + -66.954811, + 24.544245 + ], + [ + -124.733253, + 24.544245 + ] + ] + ] + ], + "type": "MultiPolygon" + }, + "theme": [ + "National Center for Health Statistics" + ], + "title": "Provisional COVID-19 death counts and rates, by jurisdiction of residence and demographic characteristics", "type": "dataset" }, { - "_score": 20.726263, + "_score": 10.183443, "_sort": [ - 1790127104708, - 20.726263, - 0, - "d95acbf8-9537-4b51-a9e0-ae0c575adc99" - ], - "dcat": { - "@type": "dcat:Dataset", - "accessLevel": "public", - "accrualPeriodicity": "irregular", - "bureauCode": [ - "026:00" - ], - "contactPoint": { - "@type": "vcard:Contact", - "fn": "MARK SCHWABACHER", - "hasEmail": "mailto:mark.a.schwabacher@nasa.gov" - }, - "description": "Modern space propulsion and exploration system designs are becoming increasingly\r\nsophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by\r\nanalyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. Inductive Monitoring System uses a data mining technique called clustering to analyze archived system data and characterize\r\nnormal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or\r\nfor analysis of archived events. Ongoing and developing Inductive Monitoring System space operations applications include International Space Station flight control, spacecraft vehicle\r\nsystem health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the Inductive Monitoring\r\nSystem data-driven technique as related to several Integrated Systems Health Management elements. Thematically, the projects listed will be used as case studies. The maturation of Inductive Monitoring System via projects where it has been deployed or is currently being\r\nintegrated to aid in fault detection will be described. The paper will also explain how Inductive Monitoring System can be used to complement a suite of other Integrated System Health Management tools, providing initial fault detection support for diagnosis and recovery.", - "distribution": [ - { - "@type": "dcat:Distribution", - "description": "IMS JACIC.pdf", - "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/IMS_JACIC.pdf", - "format": "PDF", - "mediaType": "application/pdf", - "title": "IMS JACIC.pdf" - } - ], - "identifier": "DASHLINK_669", - "issued": "2013-02-01", - "keyword": [ - "ames", - "dashlink", - "nasa" - ], - "landingPage": "https://c3.nasa.gov/dashlink/resources/669/", - "modified": "2025-03-31", - "programCode": [ - "026:029" - ], - "publisher": { - "@type": "org:Organization", - "name": "Dashlink" - }, - "title": "General Purpose Data-Driven System Monitoring for Space Operations" - }, - "description": "Modern space propulsion and exploration system designs are becoming increasingly\r\nsophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by\r\nanalyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. Inductive Monitoring System uses a data mining technique called clustering to analyze archived system data and characterize\r\nnormal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or\r\nfor analysis of archived events. Ongoing and developing Inductive Monitoring System space operations applications include International Space Station flight control, spacecraft vehicle\r\nsystem health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the Inductive Monitoring\r\nSystem data-driven technique as related to several Integrated Systems Health Management elements. Thematically, the projects listed will be used as case studies. The maturation of Inductive Monitoring System via projects where it has been deployed or is currently being\r\nintegrated to aid in fault detection will be described. 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The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The article describes nine anomalies detected by the four algorithms. The four algorithms use four different definitions of anomalousness. Orca uses a nearest-neighbor approach, defining a point to be an anomaly if its nearest neighbors in the data space are far away from it. The Inductive Monitoring System clusters the training data, and then uses the distance to the nearest cluster as its measure of anomalousness. GritBot learns rules from the training data, and then classifies points as anomalous if they violate these rules. One-class support vector machines map the data into a high-dimensional space in which most of the normal points are on one side of a hyperplane, and then classify points on the other side of the hyperplane as anomalous. 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As a ubiquitous environmental organism that is occasionally part of the human flora Pseudomonas aeruginosa could pose a health hazard for the immuno-compromised astronauts. In order to gain insights in the behavior of P. aeruginosa in spaceflight conditions two spaceflight-analogue culture systems i.e. the rotating wall vessel (RWV) and the random position machine (RPM) were used. Microarray analysis of P. aeruginosa PAO1 grown in the low shear modeled microgravity (LSMMG) environment of the RWV compared to the normal gravity control (NG) revealed a regulatory role for AlgU (RpoE). Specifically P. aeruginosa cultured in LSMMG exhibited increased alginate production and up-regulation of AlgU-controlled transcripts including those encoding stress-related proteins. This study also shows the involvement of Hfq in the LSMMG response consistent with its previously identified role in the Salmonella LSMMG- and spaceflight response. Furthermore cultivation in LSMMG increased heat and oxidative stress resistance and caused a decrease in the culture oxygen transfer rate. Interestingly the global transcriptional response of P. aeruginosa grown in the RPM was similar to that in NG. The possible role of differences in fluid mixing between the RWV and RPM is discussed with the overall collective data favoring the RWV as the optimal model to study the LSMMG-response of suspended cells. This study represents a first step towards the identification of specific virulence mechanisms of P. aeruginosa act